Overview

Dataset statistics

Number of variables22
Number of observations4399
Missing cells6887
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory756.2 KiB
Average record size in memory176.0 B

Variable types

Numeric18
Categorical3
DateTime1

Alerts

grade is highly correlated with sqft_basement and 5 other fieldsHigh correlation
sqft_basement is highly correlated with grade and 5 other fieldsHigh correlation
bathrooms is highly correlated with grade and 5 other fieldsHigh correlation
bedrooms is highly correlated with sqft_basement and 4 other fieldsHigh correlation
sqft_above is highly correlated with grade and 7 other fieldsHigh correlation
sqft_living15 is highly correlated with grade and 3 other fieldsHigh correlation
floors is highly correlated with bedrooms and 3 other fieldsHigh correlation
yr_renovated is highly correlated with jhygtfHigh correlation
yr_built is highly correlated with zipcode and 3 other fieldsHigh correlation
jhygtf is highly correlated with yr_renovatedHigh correlation
sqft_lot is highly correlated with sqft_lot15High correlation
price is highly correlated with bathrooms and 2 other fieldsHigh correlation
sqft_lot15 is highly correlated with sqft_lotHigh correlation
sqft_living is highly correlated with grade and 7 other fieldsHigh correlation
view is highly correlated with waterfrontHigh correlation
waterfront is highly correlated with viewHigh correlation
zipcode is highly correlated with yr_builtHigh correlation
condition is highly correlated with yr_builtHigh correlation
sqft_basement has 2677 (60.9%) missing values Missing
yr_renovated has 4187 (95.2%) missing values Missing
df_index has unique values Unique
jhygtf has 4184 (95.1%) zeros Zeros

Reproduction

Analysis started2022-09-21 03:00:48.151695
Analysis finished2022-09-21 03:02:20.816947
Duration1 minute and 32.67 seconds
Software versionpandas-profiling v3.3.0
Download configurationconfig.json

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct4399
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18666.95044
Minimum14
Maximum111906
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:21.143974image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile1161.6
Q16273
median14079
Q326831.5
95-th percentile52858.4
Maximum111906
Range111892
Interquartile range (IQR)20558.5

Descriptive statistics

Standard deviation16363.50712
Coefficient of variation (CV)0.8766031263
Kurtosis1.897401415
Mean18666.95044
Median Absolute Deviation (MAD)9393
Skewness1.353343821
Sum82115915
Variance267764365.2
MonotonicityNot monotonic
2022-09-20T22:02:21.439066image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
328281
 
< 0.1%
97971
 
< 0.1%
276721
 
< 0.1%
267901
 
< 0.1%
488441
 
< 0.1%
269711
 
< 0.1%
35631
 
< 0.1%
106761
 
< 0.1%
246091
 
< 0.1%
81691
 
< 0.1%
Other values (4389)4389
99.8%
ValueCountFrequency (%)
141
< 0.1%
151
< 0.1%
181
< 0.1%
201
< 0.1%
271
< 0.1%
291
< 0.1%
301
< 0.1%
311
< 0.1%
411
< 0.1%
421
< 0.1%
ValueCountFrequency (%)
1119061
< 0.1%
1033401
< 0.1%
937921
< 0.1%
919321
< 0.1%
916731
< 0.1%
851471
< 0.1%
851261
< 0.1%
838691
< 0.1%
837881
< 0.1%
837461
< 0.1%

zipcode
Real number (ℝ≥0)

HIGH CORRELATION

Distinct70
Distinct (%)1.6%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean98077.5165
Minimum98001
Maximum98199
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:21.753886image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum98001
5-th percentile98004
Q198032
median98065
Q398117
95-th percentile98177
Maximum98199
Range198
Interquartile range (IQR)85

Descriptive statistics

Standard deviation53.65570198
Coefficient of variation (CV)0.0005470744355
Kurtosis-0.8578788485
Mean98077.5165
Median Absolute Deviation (MAD)42
Skewness0.4100925325
Sum431050685
Variance2878.934355
MonotonicityNot monotonic
2022-09-20T22:02:22.065414image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
98103129
 
2.9%
98117129
 
2.9%
98052127
 
2.9%
98115124
 
2.8%
98023117
 
2.7%
98034112
 
2.5%
98118106
 
2.4%
98133104
 
2.4%
98042104
 
2.4%
9803399
 
2.3%
Other values (60)3244
73.7%
ValueCountFrequency (%)
9800188
2.0%
9800243
1.0%
9800349
1.1%
9800467
1.5%
9800541
0.9%
9800693
2.1%
9800720
 
0.5%
9800863
1.4%
9801014
 
0.3%
9801146
1.0%
ValueCountFrequency (%)
9819964
1.5%
9819858
1.3%
9818822
 
0.5%
9817861
1.4%
9817755
1.3%
9816852
1.2%
9816646
1.0%
9815593
2.1%
9814813
 
0.3%
9814650
1.1%

grade
Real number (ℝ≥0)

HIGH CORRELATION

Distinct11
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean7.655070487
Minimum3
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:22.326721image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile6
Q17
median7
Q38
95-th percentile10
Maximum13
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.180323667
Coefficient of variation (CV)0.154188478
Kurtosis1.078089479
Mean7.655070487
Median Absolute Deviation (MAD)1
Skewness0.7560926603
Sum33667
Variance1.393163959
MonotonicityNot monotonic
2022-09-20T22:02:22.517736image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
71834
41.7%
81230
28.0%
9510
 
11.6%
6407
 
9.3%
10256
 
5.8%
1177
 
1.8%
560
 
1.4%
1216
 
0.4%
44
 
0.1%
133
 
0.1%
ValueCountFrequency (%)
31
 
< 0.1%
44
 
0.1%
560
 
1.4%
6407
 
9.3%
71834
41.7%
81230
28.0%
9510
 
11.6%
10256
 
5.8%
1177
 
1.8%
1216
 
0.4%
ValueCountFrequency (%)
133
 
0.1%
1216
 
0.4%
1177
 
1.8%
10256
 
5.8%
9510
 
11.6%
81230
28.0%
71834
41.7%
6407
 
9.3%
560
 
1.4%
44
 
0.1%

sqft_basement
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct202
Distinct (%)11.7%
Missing2677
Missing (%)60.9%
Infinite0
Infinite (%)0.0%
Mean741.4982578
Minimum10
Maximum3480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:22.766747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile190
Q1450
median700
Q3970
95-th percentile1459.5
Maximum3480
Range3470
Interquartile range (IQR)520

Descriptive statistics

Standard deviation399.378006
Coefficient of variation (CV)0.5386095001
Kurtosis2.637427297
Mean741.4982578
Median Absolute Deviation (MAD)260
Skewness1.038696699
Sum1276860
Variance159502.7917
MonotonicityNot monotonic
2022-09-20T22:02:23.093517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
70049
 
1.1%
50048
 
1.1%
60047
 
1.1%
40036
 
0.8%
80035
 
0.8%
90033
 
0.8%
30033
 
0.8%
100031
 
0.7%
62025
 
0.6%
110022
 
0.5%
Other values (192)1363
31.0%
(Missing)2677
60.9%
ValueCountFrequency (%)
101
 
< 0.1%
201
 
< 0.1%
401
 
< 0.1%
601
 
< 0.1%
703
 
0.1%
805
0.1%
902
 
< 0.1%
1009
0.2%
1103
 
0.1%
1204
0.1%
ValueCountFrequency (%)
34801
< 0.1%
32601
< 0.1%
27301
< 0.1%
23301
< 0.1%
22202
< 0.1%
21601
< 0.1%
21501
< 0.1%
21001
< 0.1%
20601
< 0.1%
20401
< 0.1%

view
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size257.9 KiB
0.0
3980 
2.0
 
189
3.0
 
90
1.0
 
72
4.0
 
68

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13197
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.03980
90.5%
2.0189
 
4.3%
3.090
 
2.0%
1.072
 
1.6%
4.068
 
1.5%

Length

2022-09-20T22:02:23.554550image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T22:02:23.769567image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.03980
90.5%
2.0189
 
4.3%
3.090
 
2.0%
1.072
 
1.6%
4.068
 
1.5%

Most occurring characters

ValueCountFrequency (%)
08379
63.5%
.4399
33.3%
2189
 
1.4%
390
 
0.7%
172
 
0.5%
468
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8798
66.7%
Other Punctuation4399
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08379
95.2%
2189
 
2.1%
390
 
1.0%
172
 
0.8%
468
 
0.8%
Other Punctuation
ValueCountFrequency (%)
.4399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13197
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08379
63.5%
.4399
33.3%
2189
 
1.4%
390
 
0.7%
172
 
0.5%
468
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII13197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08379
63.5%
.4399
33.3%
2189
 
1.4%
390
 
0.7%
172
 
0.5%
468
 
0.5%

bathrooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct25
Distinct (%)0.6%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2.112349329
Minimum0.5
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:23.972582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0.5
5-th percentile1
Q11.75
median2.25
Q32.5
95-th percentile3.5
Maximum8
Range7.5
Interquartile range (IQR)0.75

Descriptive statistics

Standard deviation0.7714183773
Coefficient of variation (CV)0.3651945096
Kurtosis1.610740405
Mean2.112349329
Median Absolute Deviation (MAD)0.5
Skewness0.5463799915
Sum9288
Variance0.5950863129
MonotonicityNot monotonic
2022-09-20T22:02:24.183466image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2.51066
24.2%
1798
18.1%
1.75641
14.6%
2.25424
 
9.6%
2392
 
8.9%
1.5268
 
6.1%
2.75249
 
5.7%
3.5159
 
3.6%
3154
 
3.5%
3.25116
 
2.6%
Other values (15)130
 
3.0%
ValueCountFrequency (%)
0.51
 
< 0.1%
0.7516
 
0.4%
1798
18.1%
1.254
 
0.1%
1.5268
 
6.1%
1.75641
14.6%
2392
 
8.9%
2.25424
 
9.6%
2.51066
24.2%
2.75249
 
5.7%
ValueCountFrequency (%)
81
 
< 0.1%
7.51
 
< 0.1%
61
 
< 0.1%
5.751
 
< 0.1%
5.51
 
< 0.1%
5.254
 
0.1%
53
 
0.1%
4.755
 
0.1%
4.518
0.4%
4.2514
0.3%

bedrooms
Real number (ℝ≥0)

HIGH CORRELATION

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.367810866
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:24.376670image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median3
Q34
95-th percentile5
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.9076104406
Coefficient of variation (CV)0.2694956684
Kurtosis1.191415866
Mean3.367810866
Median Absolute Deviation (MAD)1
Skewness0.40512214
Sum14815
Variance0.8237567118
MonotonicityNot monotonic
2022-09-20T22:02:24.562687image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
31945
44.2%
41426
32.4%
2581
 
13.2%
5340
 
7.7%
149
 
1.1%
646
 
1.0%
79
 
0.2%
92
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
149
 
1.1%
2581
 
13.2%
31945
44.2%
41426
32.4%
5340
 
7.7%
646
 
1.0%
79
 
0.2%
81
 
< 0.1%
92
 
< 0.1%
ValueCountFrequency (%)
92
 
< 0.1%
81
 
< 0.1%
79
 
0.2%
646
 
1.0%
5340
 
7.7%
41426
32.4%
31945
44.2%
2581
 
13.2%
149
 
1.1%

sqft_above
Real number (ℝ≥0)

HIGH CORRELATION

Distinct490
Distinct (%)11.1%
Missing4
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean1776.286234
Minimum420
Maximum8570
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:24.810570image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile850
Q11200
median1560
Q32190
95-th percentile3343
Maximum8570
Range8150
Interquartile range (IQR)990

Descriptive statistics

Standard deviation807.3587155
Coefficient of variation (CV)0.4545206172
Kurtosis3.094953254
Mean1776.286234
Median Absolute Deviation (MAD)440
Skewness1.369814349
Sum7806778
Variance651828.0956
MonotonicityNot monotonic
2022-09-20T22:02:25.058584image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
122046
 
1.0%
129044
 
1.0%
130044
 
1.0%
125041
 
0.9%
101041
 
0.9%
128041
 
0.9%
112040
 
0.9%
115039
 
0.9%
120039
 
0.9%
134039
 
0.9%
Other values (480)3981
90.5%
ValueCountFrequency (%)
4201
 
< 0.1%
4701
 
< 0.1%
5201
 
< 0.1%
5301
 
< 0.1%
5401
 
< 0.1%
5501
 
< 0.1%
5602
< 0.1%
5703
0.1%
5801
 
< 0.1%
5903
0.1%
ValueCountFrequency (%)
85701
< 0.1%
78801
< 0.1%
60901
< 0.1%
58301
< 0.1%
56701
< 0.1%
54901
< 0.1%
53701
< 0.1%
51801
< 0.1%
51601
< 0.1%
50701
< 0.1%

sqft_living15
Real number (ℝ≥0)

HIGH CORRELATION

Distinct441
Distinct (%)10.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1988.22965
Minimum720
Maximum5790
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:25.316890image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum720
5-th percentile1140
Q11500
median1830
Q32360
95-th percentile3330
Maximum5790
Range5070
Interquartile range (IQR)860

Descriptive statistics

Standard deviation680.6366506
Coefficient of variation (CV)0.342333015
Kurtosis1.445975461
Mean1988.22965
Median Absolute Deviation (MAD)400
Skewness1.090457809
Sum8744234
Variance463266.2502
MonotonicityNot monotonic
2022-09-20T22:02:25.563484image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
161044
 
1.0%
156043
 
1.0%
164039
 
0.9%
154039
 
0.9%
139038
 
0.9%
172038
 
0.9%
152037
 
0.8%
144037
 
0.8%
158037
 
0.8%
166037
 
0.8%
Other values (431)4009
91.1%
ValueCountFrequency (%)
7201
 
< 0.1%
7501
 
< 0.1%
7601
 
< 0.1%
7802
< 0.1%
8202
< 0.1%
8281
 
< 0.1%
8302
< 0.1%
8403
0.1%
8501
 
< 0.1%
8602
< 0.1%
ValueCountFrequency (%)
57902
< 0.1%
53401
< 0.1%
51101
< 0.1%
50701
< 0.1%
49202
< 0.1%
47602
< 0.1%
47001
< 0.1%
46701
< 0.1%
46302
< 0.1%
45901
< 0.1%

lat
Real number (ℝ≥0)

Distinct2807
Distinct (%)63.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4454.140702
Minimum47.1647
Maximum47776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:25.826493image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum47.1647
5-th percentile47.3098
Q147.48775
median47.6057
Q347.7011
95-th percentile47548.3
Maximum47776
Range47728.8353
Interquartile range (IQR)0.21335

Descriptive statistics

Standard deviation13783.62797
Coefficient of variation (CV)3.094565011
Kurtosis5.892520644
Mean4454.140702
Median Absolute Deviation (MAD)0.1051
Skewness2.80886717
Sum19593764.95
Variance189988400
MonotonicityNot monotonic
2022-09-20T22:02:26.087501image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47.66246
 
0.1%
47.53966
 
0.1%
47.68866
 
0.1%
476816
 
0.1%
47.58186
 
0.1%
47.71685
 
0.1%
47.69345
 
0.1%
47.68535
 
0.1%
47.68425
 
0.1%
47.545
 
0.1%
Other values (2797)4344
98.7%
ValueCountFrequency (%)
47.16471
< 0.1%
47.17751
< 0.1%
47.17761
< 0.1%
47.17951
< 0.1%
47.19131
< 0.1%
47.19371
< 0.1%
47.19381
< 0.1%
47.19411
< 0.1%
47.19431
< 0.1%
47.19551
< 0.1%
ValueCountFrequency (%)
477761
 
< 0.1%
477744
0.1%
477722
< 0.1%
477711
 
< 0.1%
477621
 
< 0.1%
477591
 
< 0.1%
477571
 
< 0.1%
477542
< 0.1%
477531
 
< 0.1%
477522
< 0.1%

waterfront
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size257.9 KiB
0.0
4364 
1.0
 
35

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13197
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.04364
99.2%
1.035
 
0.8%

Length

2022-09-20T22:02:26.333517image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T22:02:26.527535image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
0.04364
99.2%
1.035
 
0.8%

Most occurring characters

ValueCountFrequency (%)
08763
66.4%
.4399
33.3%
135
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8798
66.7%
Other Punctuation4399
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
08763
99.6%
135
 
0.4%
Other Punctuation
ValueCountFrequency (%)
.4399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13197
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
08763
66.4%
.4399
33.3%
135
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII13197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
08763
66.4%
.4399
33.3%
135
 
0.3%

floors
Real number (ℝ≥0)

HIGH CORRELATION

Distinct6
Distinct (%)0.1%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean1.484762338
Minimum1
Maximum3.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:26.673544image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile2
Maximum3.5
Range2.5
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.5390735904
Coefficient of variation (CV)0.3630706253
Kurtosis-0.4506863326
Mean1.484762338
Median Absolute Deviation (MAD)0
Skewness0.6499864076
Sum6528.5
Variance0.2906003359
MonotonicityNot monotonic
2022-09-20T22:02:26.845560image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
12212
50.3%
21637
37.2%
1.5391
 
8.9%
3125
 
2.8%
2.531
 
0.7%
3.51
 
< 0.1%
(Missing)2
 
< 0.1%
ValueCountFrequency (%)
12212
50.3%
1.5391
 
8.9%
21637
37.2%
2.531
 
0.7%
3125
 
2.8%
3.51
 
< 0.1%
ValueCountFrequency (%)
3.51
 
< 0.1%
3125
 
2.8%
2.531
 
0.7%
21637
37.2%
1.5391
 
8.9%
12212
50.3%

yr_renovated
Real number (ℝ≥0)

HIGH CORRELATION
MISSING

Distinct52
Distinct (%)24.5%
Missing4187
Missing (%)95.2%
Infinite0
Infinite (%)0.0%
Mean1995.830189
Minimum1940
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:27.080572image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1940
5-th percentile1966.65
Q11987
median1998
Q32007
95-th percentile2014
Maximum2015
Range75
Interquartile range (IQR)20

Descriptive statistics

Standard deviation15.06162488
Coefficient of variation (CV)0.007546546277
Kurtosis1.266502905
Mean1995.830189
Median Absolute Deviation (MAD)10
Skewness-1.076228714
Sum423116
Variance226.852544
MonotonicityNot monotonic
2022-09-20T22:02:27.338042image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201415
 
0.3%
200710
 
0.2%
201310
 
0.2%
20049
 
0.2%
20029
 
0.2%
19988
 
0.2%
19918
 
0.2%
20108
 
0.2%
20058
 
0.2%
20007
 
0.2%
Other values (42)120
 
2.7%
(Missing)4187
95.2%
ValueCountFrequency (%)
19401
 
< 0.1%
19451
 
< 0.1%
19502
< 0.1%
19551
 
< 0.1%
19571
 
< 0.1%
19582
< 0.1%
19653
0.1%
19681
 
< 0.1%
19702
< 0.1%
19721
 
< 0.1%
ValueCountFrequency (%)
20154
 
0.1%
201415
0.3%
201310
0.2%
20122
 
< 0.1%
20115
 
0.1%
20108
0.2%
20093
 
0.1%
20081
 
< 0.1%
200710
0.2%
20065
 
0.1%

yr_built
Real number (ℝ≥0)

HIGH CORRELATION

Distinct116
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1970.903842
Minimum1900
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:27.611060image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum1900
5-th percentile1916
Q11951
median1974
Q31996
95-th percentile2011
Maximum2015
Range115
Interquartile range (IQR)45

Descriptive statistics

Standard deviation29.16811726
Coefficient of variation (CV)0.0147993609
Kurtosis-0.6648264868
Mean1970.903842
Median Absolute Deviation (MAD)22
Skewness-0.4507994939
Sum8670006
Variance850.7790644
MonotonicityNot monotonic
2022-09-20T22:02:27.878082image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2014122
 
2.8%
200592
 
2.1%
200491
 
2.1%
197787
 
2.0%
196884
 
1.9%
197884
 
1.9%
200683
 
1.9%
200779
 
1.8%
200879
 
1.8%
196277
 
1.8%
Other values (106)3521
80.0%
ValueCountFrequency (%)
190012
0.3%
19017
 
0.2%
19023
 
0.1%
190310
0.2%
190411
0.3%
190512
0.3%
190616
0.4%
190711
0.3%
190822
0.5%
190922
0.5%
ValueCountFrequency (%)
201510
 
0.2%
2014122
2.8%
201340
 
0.9%
201237
 
0.8%
201115
 
0.3%
201024
 
0.5%
200948
 
1.1%
200879
1.8%
200779
1.8%
200683
1.9%

long
Real number (ℝ)

Distinct575
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-110838.0874
Minimum-122505
Maximum-121.76
Zeros0
Zeros (%)0.0%
Negative4399
Negative (%)100.0%
Memory size34.5 KiB
2022-09-20T22:02:28.352116image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum-122505
5-th percentile-122386
Q1-122321
median-122212
Q3-122082.5
95-th percentile-122.279
Maximum-121.76
Range122383.24
Interquartile range (IQR)238.5

Descriptive statistics

Standard deviation35499.48961
Coefficient of variation (CV)-0.3202824087
Kurtosis5.839820182
Mean-110838.0874
Median Absolute Deviation (MAD)113
Skewness2.799465297
Sum-487576746.5
Variance1260213763
MonotonicityNot monotonic
2022-09-20T22:02:28.614365image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-12236227
 
0.6%
-12229125
 
0.6%
-12230124
 
0.5%
-12235223
 
0.5%
-12231123
 
0.5%
-12236123
 
0.5%
-12229822
 
0.5%
-12239122
 
0.5%
-12228822
 
0.5%
-12235422
 
0.5%
Other values (565)4166
94.7%
ValueCountFrequency (%)
-1225052
< 0.1%
-1224821
 
< 0.1%
-1224791
 
< 0.1%
-1224731
 
< 0.1%
-1224721
 
< 0.1%
-1224631
 
< 0.1%
-1224623
0.1%
-1224561
 
< 0.1%
-1224551
 
< 0.1%
-1224521
 
< 0.1%
ValueCountFrequency (%)
-121.761
 
< 0.1%
-121.771
 
< 0.1%
-121.781
 
< 0.1%
-121.862
< 0.1%
-121.871
 
< 0.1%
-121.882
< 0.1%
-121.894
0.1%
-121.91
 
< 0.1%
-121.911
 
< 0.1%
-121.922
< 0.1%

jhygtf
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct53
Distinct (%)1.2%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean96.25022748
Minimum0
Maximum2015
Zeros4184
Zeros (%)95.1%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:28.895799image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum2015
Range2015
Interquartile range (IQR)0

Descriptive statistics

Standard deviation427.6534754
Coefficient of variation (CV)4.44314249
Kurtosis15.81063447
Mean96.25022748
Median Absolute Deviation (MAD)0
Skewness4.219261092
Sum423116
Variance182887.4951
MonotonicityNot monotonic
2022-09-20T22:02:29.164610image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04184
95.1%
201415
 
0.3%
201310
 
0.2%
200710
 
0.2%
20029
 
0.2%
20049
 
0.2%
20058
 
0.2%
20108
 
0.2%
19988
 
0.2%
19918
 
0.2%
Other values (43)127
 
2.9%
ValueCountFrequency (%)
04184
95.1%
19401
 
< 0.1%
19451
 
< 0.1%
19502
 
< 0.1%
19551
 
< 0.1%
19571
 
< 0.1%
19582
 
< 0.1%
19653
 
0.1%
19681
 
< 0.1%
19702
 
< 0.1%
ValueCountFrequency (%)
20154
 
0.1%
201415
0.3%
201310
0.2%
20122
 
< 0.1%
20115
 
0.1%
20108
0.2%
20093
 
0.1%
20081
 
< 0.1%
200710
0.2%
20065
 
0.1%

sqft_lot
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2950
Distinct (%)67.1%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean14267.01819
Minimum609
Maximum1074218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:29.449581image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum609
5-th percentile1881.4
Q15082
median7684.5
Q310693
95-th percentile40444.35
Maximum1074218
Range1073609
Interquartile range (IQR)5611

Descriptive statistics

Standard deviation36474.92835
Coefficient of variation (CV)2.556590863
Kurtosis268.1891864
Mean14267.01819
Median Absolute Deviation (MAD)2684.5
Skewness12.94256235
Sum62746346
Variance1330420398
MonotonicityNot monotonic
2022-09-20T22:02:29.713602image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
600063
 
1.4%
500063
 
1.4%
400048
 
1.1%
720042
 
1.0%
480038
 
0.9%
450024
 
0.5%
750023
 
0.5%
840021
 
0.5%
900021
 
0.5%
960021
 
0.5%
Other values (2940)4034
91.7%
ValueCountFrequency (%)
6091
< 0.1%
6351
< 0.1%
6381
< 0.1%
7001
< 0.1%
7111
< 0.1%
7472
< 0.1%
7621
< 0.1%
7801
< 0.1%
8121
< 0.1%
8191
< 0.1%
ValueCountFrequency (%)
10742181
< 0.1%
8712001
< 0.1%
5070381
< 0.1%
4935341
< 0.1%
4538951
< 0.1%
4320361
< 0.1%
4238381
< 0.1%
3299031
< 0.1%
2863551
< 0.1%
2809621
< 0.1%

price
Real number (ℝ≥0)

HIGH CORRELATION

Distinct1554
Distinct (%)35.4%
Missing3
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean40178389.43
Minimum89950
Maximum3635000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:30.006461image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum89950
5-th percentile210000
Q1319950
median450000
Q3650000
95-th percentile1250000
Maximum3635000000
Range3634910050
Interquartile range (IQR)330050

Descriptive statistics

Standard deviation245974676.2
Coefficient of variation (CV)6.122064118
Kurtosis50.09732109
Mean40178389.43
Median Absolute Deviation (MAD)153000
Skewness6.748377121
Sum1.766241999 × 1011
Variance6.050354135 × 1016
MonotonicityNot monotonic
2022-09-20T22:02:30.267751image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35000043
 
1.0%
45000041
 
0.9%
37500036
 
0.8%
32500035
 
0.8%
52500034
 
0.8%
40000031
 
0.7%
50000030
 
0.7%
25000030
 
0.7%
44000029
 
0.7%
47500028
 
0.6%
Other values (1544)4059
92.3%
ValueCountFrequency (%)
899501
< 0.1%
1055001
< 0.1%
1060001
< 0.1%
1100002
< 0.1%
1113001
< 0.1%
1140001
< 0.1%
1150001
< 0.1%
1218001
< 0.1%
1220001
< 0.1%
1230001
< 0.1%
ValueCountFrequency (%)
36350000001
< 0.1%
25460000001
< 0.1%
25380000001
< 0.1%
25320000001
< 0.1%
24580000001
< 0.1%
24150000001
< 0.1%
23670000001
< 0.1%
22880000001
< 0.1%
21930000001
< 0.1%
21250000001
< 0.1%

condition
Categorical

HIGH CORRELATION

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size257.9 KiB
3.0
2893 
4.0
1116 
5.0
348 
2.0
 
35
1.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters13197
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row3.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.02893
65.8%
4.01116
 
25.4%
5.0348
 
7.9%
2.035
 
0.8%
1.07
 
0.2%

Length

2022-09-20T22:02:30.509763image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram of lengths of the category

Category Frequency Plot

2022-09-20T22:02:30.720782image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
ValueCountFrequency (%)
3.02893
65.8%
4.01116
 
25.4%
5.0348
 
7.9%
2.035
 
0.8%
1.07
 
0.2%

Most occurring characters

ValueCountFrequency (%)
.4399
33.3%
04399
33.3%
32893
21.9%
41116
 
8.5%
5348
 
2.6%
235
 
0.3%
17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number8798
66.7%
Other Punctuation4399
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
04399
50.0%
32893
32.9%
41116
 
12.7%
5348
 
4.0%
235
 
0.4%
17
 
0.1%
Other Punctuation
ValueCountFrequency (%)
.4399
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common13197
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
.4399
33.3%
04399
33.3%
32893
21.9%
41116
 
8.5%
5348
 
2.6%
235
 
0.3%
17
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII13197
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
.4399
33.3%
04399
33.3%
32893
21.9%
41116
 
8.5%
5348
 
2.6%
235
 
0.3%
17
 
0.1%

sqft_lot15
Real number (ℝ≥0)

HIGH CORRELATION

Distinct2791
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12271.97977
Minimum748
Maximum871200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:30.988801image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum748
5-th percentile2138.8
Q15110
median7688
Q310077.5
95-th percentile35900.6
Maximum871200
Range870452
Interquartile range (IQR)4967.5

Descriptive statistics

Standard deviation26827.1341
Coefficient of variation (CV)2.186047778
Kurtosis304.5097131
Mean12271.97977
Median Absolute Deviation (MAD)2488
Skewness13.29356308
Sum53984439
Variance719695124.1
MonotonicityNot monotonic
2022-09-20T22:02:31.243724image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500087
 
2.0%
400071
 
1.6%
600067
 
1.5%
720033
 
0.8%
480032
 
0.7%
800027
 
0.6%
750023
 
0.5%
450023
 
0.5%
900021
 
0.5%
408021
 
0.5%
Other values (2781)3994
90.8%
ValueCountFrequency (%)
7481
 
< 0.1%
8171
 
< 0.1%
8241
 
< 0.1%
8991
 
< 0.1%
9251
 
< 0.1%
9281
 
< 0.1%
9423
0.1%
9531
 
< 0.1%
9671
 
< 0.1%
9761
 
< 0.1%
ValueCountFrequency (%)
8712001
< 0.1%
5606171
< 0.1%
3253931
< 0.1%
2634921
< 0.1%
2308681
< 0.1%
2291251
< 0.1%
2234631
< 0.1%
2225911
< 0.1%
2202321
< 0.1%
2195422
< 0.1%

sqft_living
Real number (ℝ≥0)

HIGH CORRELATION

Distinct539
Distinct (%)12.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean2066.738972
Minimum420
Maximum12050
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.5 KiB
2022-09-20T22:02:31.509747image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Quantile statistics

Minimum420
5-th percentile940
Q11420
median1900
Q32550
95-th percentile3750
Maximum12050
Range11630
Interquartile range (IQR)1130

Descriptive statistics

Standard deviation897.1812225
Coefficient of variation (CV)0.4341047586
Kurtosis5.320023485
Mean2066.738972
Median Absolute Deviation (MAD)550
Skewness1.384835176
Sum9089518
Variance804934.1461
MonotonicityNot monotonic
2022-09-20T22:02:31.763929image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190037
 
0.8%
182036
 
0.8%
144035
 
0.8%
166034
 
0.8%
130033
 
0.8%
128033
 
0.8%
154031
 
0.7%
172030
 
0.7%
174030
 
0.7%
194029
 
0.7%
Other values (529)4070
92.5%
ValueCountFrequency (%)
4201
< 0.1%
4701
< 0.1%
5201
< 0.1%
5301
< 0.1%
5401
< 0.1%
5602
< 0.1%
5701
< 0.1%
5902
< 0.1%
6002
< 0.1%
6301
< 0.1%
ValueCountFrequency (%)
120501
< 0.1%
78801
< 0.1%
77101
< 0.1%
70501
< 0.1%
65101
< 0.1%
65001
< 0.1%
62001
< 0.1%
59901
< 0.1%
58601
< 0.1%
58301
< 0.1%

date
Date

Distinct329
Distinct (%)7.5%
Missing1
Missing (%)< 0.1%
Memory size34.5 KiB
Minimum2014-05-02 00:00:00
Maximum2015-05-24 00:00:00
2022-09-20T22:02:32.050725image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
2022-09-20T22:02:32.307745image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

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Correlations

2022-09-20T22:02:32.799447image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-09-20T22:02:33.227603image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-09-20T22:02:33.661582image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-09-20T22:02:34.056608image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-09-20T22:02:34.312625image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-09-20T22:02:18.670007image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
A simple visualization of nullity by column.
2022-09-20T22:02:19.539522image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2022-09-20T22:02:20.129565image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.
2022-09-20T22:02:20.518595image/svg+xmlMatplotlib v3.5.3, https://matplotlib.org/
The dendrogram allows you to more fully correlate variable completion, revealing trends deeper than the pairwise ones visible in the correlation heatmap.

Sample

First rows

df_indexzipcodegradesqft_basementviewbathroomsbedroomssqft_abovesqft_living15latwaterfrontfloorsyr_renovatedyr_builtlongjhygtfsqft_lotpriceconditionsqft_lot15sqft_livingdate
03282898058.08.0NaN0.02.253.01780.02080.047.45390.01.0NaN1967.0-122.150.010395.0300000.03.09360.01780.02014-06-26
1601298117.08.0NaN0.02.503.01350.01350.047.67580.03.0NaN2005.0-122386.000.02053.0477000.03.04150.01350.02014-05-19
23492098028.08.0NaN0.03.004.02450.02460.047.77210.02.0NaN2004.0-122235.000.04668.0500000.03.04895.02450.02015-04-06
392398133.07.0NaN0.01.753.01420.01740.047.75350.01.0NaN1954.0-122354.000.08250.0265000.03.08000.01420.02014-08-26
4985998055.07.0240.00.01.002.01180.01490.047.43420.01.0NaN1956.0-122195.000.081892.0360000.03.01863.01420.02014-05-09
51348898052.09.0NaN0.02.504.02110.02180.047.63740.02.0NaN1999.0-122111.000.06069.0689000.03.09000.02110.02015-03-25
61597398118.07.01100.00.01.754.01100.01600.047543.00000.01.0NaN1955.0-122.280.07475.0375000.05.05766.02200.02014-06-04
72426598052.010.0NaN0.03.003.02960.02640.047.71830.02.0NaN1995.0-122.100.042159.0849000.03.025209.02960.02014-07-29
82905598003.07.0NaN0.01.753.01320.01550.047.32570.01.0NaN1956.0-122296.000.017390.0199000.04.019265.01320.02014-08-13
9255098004.011.0NaN0.03.504.04280.02360.047.59790.02.0NaN2005.0-122197.000.09583.01600000.03.010031.04280.02014-06-06

Last rows

df_indexzipcodegradesqft_basementviewbathroomsbedroomssqft_abovesqft_living15latwaterfrontfloorsyr_renovatedyr_builtlongjhygtfsqft_lotpriceconditionsqft_lot15sqft_livingdate
43894863098034.07.0NaN0.02.004.02100.01720.047.72390.01.0NaN1972.0-122173.00.012620.05.000000e+054.07840.02100.02014-08-27
439014498022.07.01580.00.01.754.02150.01880.047.17750.01.0NaN1974.0-122022.00.016980.03.600000e+054.016963.0NaN2014-08-25
43915996698116.07.0800.00.01.003.01560.01690.047.57050.01.0NaN1964.0-122384.00.05012.05.320000e+053.04800.02360.02015-04-16
43923862398005.011.0NaN0.02.754.03200.04050.047.64020.02.0NaN1984.0-122171.00.013729.01.272000e+093.016921.03200.02014-09-29
4393934398115.08.0NaN0.02.503.02060.01240.047.69610.02.02006.01924.0-122316.02006.09715.08.400000e+053.07072.02060.02014-08-20
43941437598038.07.0NaN0.02.003.01220.01570.047.35230.01.0NaN1994.0-122059.00.06404.02.499000e+053.07000.01220.02014-07-17
43952977598155.07.0800.00.02.003.01140.01940.047774.00000.01.0NaN1978.0-122283.00.016300.03.990000e+053.011250.01940.02014-08-08
4396580198022.07.01220.00.01.753.01390.02140.047.25850.01.0NaN1981.0-121925.00.0117176.03.780000e+053.0142005.02610.02014-10-20
43971661098034.07.0NaN0.01.503.01340.01620.047.71680.01.0NaN1972.0-122192.00.06500.03.870000e+053.07107.01340.02014-10-27
439823598117.06.0NaN0.01.002.0910.01480.047683.00000.01.0NaN1914.0-122374.00.05000.04.500000e+054.05000.0910.02014-08-26